InfoGram and Admissible Machine Learning

TL;DR

This paper introduces an information-theoretic framework for admissible machine learning that ensures regulatory compliance without sacrificing predictive accuracy, using tools like InfoGram and ALFA-testing across various sectors.

Contribution

It presents a novel framework and algorithms for designing fair, transparent, and regulatory-compliant ML models that retain high predictive performance.

Findings

The framework effectively guides the redesign of ML models for compliance.

Tools like InfoGram help identify regulatory constraints.

Real-data examples demonstrate practical applicability.

Abstract

We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fair, transparent and trustworthy ML methods. The purpose of this article is to introduce a new information-theoretic learning framework (admissible machine learning) and algorithmic risk-management tools (InfoGram, L-features, ALFA-testing) that can guide an analyst to redesign off-the-shelf ML methods to be regulatory compliant, while maintaining good prediction accuracy. We have illustrated our approach using several real-data examples from financial sectors, biomedical research, marketing campaigns, and the criminal justice system.